This paper presents a verification-based methodology to validate the model of personalized health conditions. The model identifies the values that may result in unsafe, un-reachable, in-exhaustive, and overlapping states that otherwise threaten patients' life by producing false alarms by accepting suspicious behaviour of the target health condition. Contemporary approaches to validating a model employ various testing, simulation and model checking techniques to recognise such values and corresponding vulnerabilities. However, these approaches are neither systematic nor exhaustive and thus fail to identify those false values or vulnerabilities that estimate the health condition at run-time based on the sensor or input data received from various IoT medical devices. We have demonstrated the validation methodology by validating our example multi-level model that describes three different scenarios of Diabetes health conditions.